Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.

Purpose: The autosegmentation algorithm of Siemens Healthineers version VA 30 (AASH) (Siemens Healthineers, Erlangen, Germany) was trained and developed in the male pelvis, with no published data on its usability in the female pelvis. This is the first multi-institutional study to describe and evaluate an artificial intelligence algorithm for autosegmentation of the pelvic nodal region by gender. Methods and Materials: We retrospectively evaluated AASH pelvic nodal autosegmentation in both male and female patients treated at our network of institutions. The automated pelvic nodal contours generated by AASH were evaluated by 1 board-certified radiation oncologist. A 4-point scale was used for each nodal region contour: a score of 4 is clinically usable with minimal edits; a score of 3 requires minor edits (missing nodal contour region, cutting through vessels, or including bowel loops) in 3 or fewer computed tomography slices; a score of 2 requires major edits, as previously defined but in 4 or more computed tomography slices; and a score of 1 requires complete recontouring of the region. Pelvic nodal regions included the right and left side of the common iliac, external iliac, internal iliac, obturator, and midline presacral nodes. In addition, patients were graded based on their lowest nodal contour score. Statistical analysis was performed using Fisher exact tests and Yates-corrected χ Results: Fifty-two female and 51 male patients were included in the study, representing a total of 468 and 447 pelvic nodal regions, respectively. Ninety-six percent and 99% of contours required minor edits at most (score of 3 or 4) for female and male patients, respectively ( Conclusions: AASH pelvic nodal autosegmentation performed very well in both male and female pelvic nodal regions, although with better male pelvic nodal autosegmentation. As autosegmentation becomes more widespread, it may be important to have equal representation from all sexes in training and validation of autosegmentation algorithms.